What is Differential Expression?

Differential Expression (DE) refers to the process of identifying and analyzing genes whose expression levels vary significantly between different biological conditions, such as disease versus healthy states, treated versus untreated samples, or any other experimental groups. The aim is to determine which genes are upregulated or downregulated in response to specific conditions.

Here’s a step-by-step guide to understanding and performing a differential expression analysis using RNA-Seq data:

Step 1: Understand the Biological Context

• Identify the biological question: What conditions are you comparing? E.g., tumor vs. healthy tissue.
• Define the experimental design: Number of replicates, sample groups, and conditions.

Step 2: Data Preparation

1. Obtain RNA-Seq data:
   • Data can be obtained from publicly available repositories like GEO or SRA.
   • Alternatively, sequence your samples.
2. Quality Control:
   • Use FastQC for assessing read quality.
   • Filter low-quality reads using Trimmomatic or Cutadapt.

Step 3: Align Reads to Reference Genome

• Tools: STAR, HISAT2, or TopHat.
• Command Example (using HISAT2):
  bash

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  hisat2 -x genome_index -U reads.fastq -S output.sam

• Convert SAM to BAM:
  bash

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  samtools view -Sb output.sam > output.bam


Step 4: Count Gene Expression

• Use tools like featureCounts or HTSeq.
• Example (using featureCounts):
  bash

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  featureCounts -a annotation.gtf -o counts.txt output.bam


Step 5: Perform Differential Expression Analysis

1. Software options:
   • DESeq2 (R)
   • edgeR (R)
   • limma (R)
   • Ballgown (Python)
2. DESeq2 Workflow in R:
   R

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   library(DESeq2)
   # Load count data and metadata
   counts <- read.csv("counts.txt", row.names = 1)
   metadata <- read.csv("metadata.csv", row.names = 1)

   # Create DESeq2 object
   dds <- DESeqDataSetFromMatrix(countData = counts, colData = metadata, design = ~ condition)

   # Run differential expression analysis
   dds <- DESeq(dds)
   results <- results(dds)

   # View results
   head(results)

   # Export significant genes
write.csv(as.data.frame(results), file = "DE_results.csv")


Step 6: Visualize Results

• Volcano Plot:
  R

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  library(ggplot2)
ggplot(data=results, aes(x=log2FoldChange, y=-log10(pvalue))) +
geom_point(alpha=0.4) +
theme_minimal() +
xlab("Log2 Fold Change") +
ylab("-Log10 p-value")

• Heatmap: Use the pheatmap package in R for clustering and visualization.

Step 7: Functional Analysis

• Use tools like DAVID, Enrichr, or GSEA to explore pathways and biological processes enriched in the differentially expressed genes.

Online Tools and Recent Software

1. Galaxy: A web-based platform for bioinformatics workflows (link).
2. iDEP: An interactive web-based tool for RNA-Seq data analysis (link).
3. T-BioInfo: Machine learning-based analysis platform (link).
4. DEBrowser: Visualization and analysis of DE results (link).

Summary

Differential expression analysis is a cornerstone of transcriptomics, offering insights into gene regulation, disease mechanisms, and therapeutic targets. The choice of tools and methods depends on your data type and expertise. For beginners, platforms like Galaxy or iDEP are user-friendly, while advanced users may prefer scripting with R or Python.